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Ultra-low power signal conditioning system for effective biopotential signal recording

Diksha Thakur, Kulbhushan Sharma, Sonal Kapila, Rajnish Sharma

2021Journal of Micromechanics and Microengineering33 citationsDOI

Abstract

Abstract Design of Low-power signal conditioning system comprising of biopotential amplifier (BPA) and low pass Filter (LPF) remains one of the most critical block of system on chips (SoCs) targeted for wearable non-invasive and semi-invasive biomedical applications like electroencephalography and electrocorticography. Design of the same is a challenging task owing to noise-power trade-off in BPA and dependency of process voltage and temperature variations on the filter circuit. We report the design of a signal conditioning system comprising of a capacitively coupled capacitive feedback (CC-CF) BPA and composite p-channel metal oxide semiconductor (PMOS) based complementary source-follower LPF. The results obtained in Cadence with standard 0.18 µ m technology and BSIM3V3 MOS models for the proposed signal conditioning system provide a DC-gain value of 36.76 dB, bandwidth of 280 mHz–174 Hz, power consumption of 24.54 µ W, supply current of 24.54 µ A, area consumption of 0.212826 mm 2 from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>±</mml:mo> </mml:math> 0.5 V supply voltage. Robustness of this signal conditioning system has been checked by performing 200 runs of monte carlo simulations. The statistical results obtained for gain of (CC-CF) BPA, complementary source follower (CSF-C) LPF and signal conditioning system show mean ( µ ) values of 39.93 dB, −3.92 dB and 35.96 dB as well as standard deviation ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>σ</mml:mi> </mml:math> ) of 135.433 mdB, 1.90 dB, and 2.00 dB respectively. The CC-CF BPA architecture, CSF-C LPF and complete signal conditioning system are expected to be used in SoCs targeted for various low-power biomedical applications.

Topics & Concepts

Signal conditioningElectronic engineeringRobustness (evolution)Computer scienceElectrical engineeringEngineeringPower (physics)PhysicsChemistryGeneBiochemistryQuantum mechanicsAnalog and Mixed-Signal Circuit DesignNeuroscience and Neural EngineeringAdvanced Memory and Neural Computing
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